Ebola outbreak 2014- 2016

ebola_2014_2016_clean <- read.csv("D:/desktop/R COde/ebola/ebola_2014_2016_clean.csv")

View(ebola_2014_2016_clean)

Basic data cleaing

Seprating the original Date column into month , date and year columns.

Ebola<-ebola_2014_2016_clean
library(dplyr)
library(tidyr)
Ebola<-Ebola %>% separate(Date, c("Month", "Date","Year"))
Ebola <- Ebola[,-c(6,10)]

head(Ebola)

Arranging the dataset with respect to the countries.

Ebola%>% arrange(Country)

Changing the column names.

colnames(Ebola)[5:10]<- c("suspected.cases","confirmed.cases","total.cases","suspected.deaths","confirmed.deaths","total.deaths")

colnames(Ebola)

 [1] "Country"          "Month"            "Date"             "Year"             "suspected.cases"  "confirmed.cases"  "total.cases"     
 [8] "suspected.deaths" "confirmed.deaths" "total.deaths"    

Creating a list of all the countries that got affected from the outbreak

country_list<-c(distinct(Ebola,Country))

country_list

$Country
 [1] Guinea         Nigeria        Sierra Leone   Liberia        Senegal        USA            Spain          Mali           United Kingdom
[10] Italy         
Levels: Guinea Italy Liberia Mali Nigeria Senegal Sierra Leone Spain United Kingdom USA

Creating total cases vs total deaths plot for each country

1) Calculating total cases and deaths in Guinea

r1 <-Ebola%>% select(total.deaths,Country,Year) %>% filter(Country == "Guinea", Year==2014) 
r2 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Guinea", Year==2015) 
r3 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Guinea", Year==2016) 

deaths_2014_Guinea <- sapply(select(r1,total.deaths),na.rm=T,max)
deaths_2015_Guinea<- sapply(select(r2,total.deaths),na.rm=T,max)-sapply(select(r1,total.deaths),na.rm=T,max)
deaths_2016_Guinea <- sapply(select(r3,total.deaths),na.rm=T,max)-sapply(select(r2,total.deaths),na.rm=T,max)


s1 <-Ebola%>% select(total.cases,Country,Year) %>% filter(Country == "Guinea", Year==2014) 
s2 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Guinea", Year==2015) 
s3 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Guinea", Year==2016) 

cases_2014_Guinea <- sapply(select(s1,total.cases),na.rm=T,max)
cases_2015_Guinea<- sapply(select(s2,total.cases),na.rm=T,max)-sapply(select(s1,total.cases),na.rm=T,max)
cases_2016_Guinea <- sapply(select(s3,total.cases),na.rm=T,max)-sapply(select(s2,total.cases),na.rm=T,max)

2)Calculating total cases and deaths in Nigeria

r4 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Nigeria", Year==2014) 
r5 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Nigeria", Year==2015) 
r6 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Nigeria", Year==2016) 

deaths_2014_Nigeria <- sapply(select(r4,total.deaths),na.rm=T,max)
deaths_2015_Nigeria<- sapply(select(r5,total.deaths),na.rm=T,max)-sapply(select(r4,total.deaths),na.rm=T,max)
deaths_2016_Nigeria <- sapply(select(r6,total.deaths),na.rm=T,max)-sapply(select(r5,total.deaths),na.rm=T,max)


s4 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Nigeria", Year==2014) 
s5 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Nigeria", Year==2015) 
s6 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Nigeria", Year==2016) 

cases_2014_Nigeria <- sapply(select(s4,total.cases),na.rm=T,max)
cases_2015_Nigeria<- sapply(select(s5,total.cases),na.rm=T,max)-sapply(select(s4,total.cases),na.rm=T,max)
cases_2016_Nigeria <- sapply(select(s6,total.cases),na.rm=T,max)-sapply(select(s5,total.cases),na.rm=T,max)

3)Calculating total cases and deaths in Sierra

r7 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Sierra Leone", Year==2014) 
r8 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Sierra Leone", Year==2015) 
r9 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Sierra Leone", Year==2016) 

deaths_2014_Sierra <- sapply(select(r7,total.deaths),na.rm=T,max)
deaths_2015_Sierra<- sapply(select(r8,total.deaths),na.rm=T,max)-sapply(select(r7,total.deaths),na.rm=T,max)
deaths_2016_Sierra <- sapply(select(r9,total.deaths),na.rm=T,max)-sapply(select(r8,total.deaths),na.rm=T,max)

s7 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Sierra Leone", Year==2014) 
s8 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Sierra Leone", Year==2015) 
s9 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Sierra Leone", Year==2016) 

cases_2014_Sierra <- sapply(select(s7,total.cases),na.rm=T,max)
cases_2015_Sierra<- sapply(select(s8,total.cases),na.rm=T,max)-sapply(select(s7,total.cases),na.rm=T,max)
cases_2016_Sierra <- sapply(select(s9,total.cases),na.rm=T,max)-sapply(select(s8,total.cases),na.rm=T,max)

4)Calculating total cases and deaths in Liberia

r10 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Liberia", Year==2014) 
r11 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Liberia", Year==2015) 
r12 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Liberia", Year==2016) 

deaths_2014_Liberia <- sapply(select(r10,total.deaths),na.rm=T,max)
deaths_2015_Liberia<- sapply(select(r11,total.deaths),na.rm=T,max)-sapply(select(r10,total.deaths),na.rm=T,max)
deaths_2016_Liberia <- sapply(select(r12,total.deaths),na.rm=T,max)-sapply(select(r11,total.deaths),na.rm=T,max)


s10 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Liberia", Year==2014) 
s11 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Liberia", Year==2015) 
s12 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Liberia", Year==2016) 

cases_2014_Liberia <- sapply(select(s10,total.cases),na.rm=T,max)
cases_2015_Liberia<- sapply(select(s11,total.cases),na.rm=T,max)-sapply(select(s10,total.cases),na.rm=T,max)
cases_2016_Liberia <- sapply(select(s12,total.cases),na.rm=T,max)-sapply(select(s11,total.cases),na.rm=T,max)

5)Calculating total cases and deaths in Senegal

r13 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Senegal", Year==2014) 
r14 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Senegal", Year==2015) 
r15 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Senegal", Year==2016)

deaths_2014_Senegal <- sapply(select(r13,total.deaths),na.rm=T,max)
deaths_2015_Senegal<- sapply(select(r14,total.deaths),na.rm=T,max)-sapply(select(r13,total.deaths),na.rm=T,max)
deaths_2016_Senegal <- sapply(select(r15,total.deaths),na.rm=T,max)-sapply(select(r14,total.deaths),na.rm=T,max)


s13 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Senegal", Year==2014) 
s14 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Senegal", Year==2015) 
s15 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Senegal", Year==2016)

cases_2014_Senegal <- sapply(select(s13,total.cases),na.rm=T,max)
cases_2015_Senegal<- sapply(select(s14,total.cases),na.rm=T,max)-sapply(select(s13,total.cases),na.rm=T,max)
cases_2016_Senegal <- sapply(select(s15,total.cases),na.rm=T,max)-sapply(select(s14,total.cases),na.rm=T,max)

6)Calculating total cases and deaths in USA

r16 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "USA", Year==2014) 
r17 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "USA", Year==2015) 
r18 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "USA", Year==2016) 

deaths_2014_USA<- sapply(select(r16,total.deaths),na.rm=T,max)
deaths_2015_USA<- sapply(select(r17,total.deaths),na.rm=T,max)-sapply(select(r15,total.deaths),na.rm=T,max)
deaths_2016_USA <- sapply(select(r18,total.deaths),na.rm=T,max)-sapply(select(r17,total.deaths),na.rm=T,max)



s16 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "USA", Year==2014) 
s17 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "USA", Year==2015) 
s18 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "USA", Year==2016) 

cases_2014_USA<- sapply(select(s16,total.cases),na.rm=T,max)
cases_2015_USA<- sapply(select(s17,total.cases),na.rm=T,max)-sapply(select(s15,total.cases),na.rm=T,max)
cases_2016_USA <- sapply(select(s18,total.cases),na.rm=T,max)-sapply(select(s17,total.cases),na.rm=T,max)

7)Calculating total cases and deaths in Spain

r19 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Spain", Year==2014) 
r20 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Spain", Year==2015) 
r21 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Spain", Year==2016) 

deaths_2014_Spain <- sapply(select(r19,total.deaths),na.rm=T,max)
deaths_2015_Spain<- sapply(select(r20,total.deaths),na.rm=T,max)-sapply(select(r19,total.deaths),na.rm=T,max)
deaths_2016_Spain <- sapply(select(r21,total.deaths),na.rm=T,max)-sapply(select(r20,total.deaths),na.rm=T,max)


s19 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Spain", Year==2014) 
s20 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Spain", Year==2015) 
s21 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Spain", Year==2016) 

cases_2014_Spain <- sapply(select(s19,total.cases),na.rm=T,max)
cases_2015_Spain<- sapply(select(s20,total.cases),na.rm=T,max)-sapply(select(s19,total.cases),na.rm=T,max)
cases_2016_Spain <- sapply(select(s21,total.cases),na.rm=T,max)-sapply(select(s20,total.cases),na.rm=T,max)

8. calculating total cases and deaths in Mali

r22 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Mali", Year==2014) 
r23 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Mali", Year==2015) 
r24 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Mali", Year==2016) 

deaths_2014_Mali <- sapply(select(r22,total.deaths),na.rm=T,max)
deaths_2015_Mali<- sapply(select(r23,total.deaths),na.rm=T,max)-sapply(select(r22,total.deaths),na.rm=T,max)
deaths_2016_Mali <- sapply(select(r24,total.deaths),na.rm=T,max)-sapply(select(r23,total.deaths),na.rm=T,max)


s22 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Mali", Year==2014) 
s23 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Mali", Year==2015) 
s24 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Mali", Year==2016) 

cases_2014_Mali <- sapply(select(s22,total.cases),na.rm=T,max)
cases_2015_Mali<- sapply(select(s23,total.cases),na.rm=T,max)-sapply(select(s22,total.cases),na.rm=T,max)
cases_2016_Mali <- sapply(select(s24,total.cases),na.rm=T,max)-sapply(select(s23,total.cases),na.rm=T,max)

9. calculating total cases and deaths in United kingdom

r25 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "United Kingdom", Year==2014) 
r26 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "United Kingdom", Year==2015) 
r27 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "United Kingdom", Year==2016) 

deaths_2014_UnitedKingdom <- sapply(select(r25,total.deaths),na.rm=T,max)
deaths_2015_UnitedKingdom <- sapply(select(r26,total.deaths),na.rm=T,max)-sapply(select(r25,total.deaths),na.rm=T,max)
deaths_2016_UnitedKingdom <- sapply(select(r26,total.deaths),na.rm=T,max)-sapply(select(r26,total.deaths),na.rm=T,max)



s25 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "United Kingdom", Year==2014) 
s26 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "United Kingdom", Year==2015) 
s27 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "United Kingdom", Year==2016) 

cases_2014_UnitedKingdom <- sapply(select(s25,total.cases),na.rm=T,max)
cases_2015_UnitedKingdom <- sapply(select(s26,total.cases),na.rm=T,max)-sapply(select(s25,total.cases),na.rm=T,max)
cases_2016_UnitedKingdom <- sapply(select(s26,total.cases),na.rm=T,max)-sapply(select(s26,total.cases),na.rm=T,max)

10. calculating total cases and deaths in United kingdom

r28 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Italy", Year==2014) 
r29 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Italy", Year==2015) 
r30 <-Ebola %>% select(total.deaths,Country,Year) %>% filter(Country == "Italy", Year==2016) 

deaths_2014_Italy <- sapply(select(r28,total.deaths),na.rm=T,max)
deaths_2015_Italy<- sapply(select(r29,total.deaths),na.rm=T,max)-sapply(select(r28,total.deaths),na.rm=T,max)
deaths_2016_Italy <- sapply(select(r30,total.deaths),na.rm=T,max)-sapply(select(r29,total.deaths),na.rm=T,max)

s28 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Italy", Year==2014) 
s29 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Italy", Year==2015) 
s30 <-Ebola %>% select(total.cases,Country,Year) %>% filter(Country == "Italy", Year==2016) 

cases_2014_Italy <- sapply(select(s28,total.cases),na.rm=T,max)
cases_2015_Italy<- sapply(select(s29,total.cases),na.rm=T,max)-sapply(select(s28,total.cases),na.rm=T,max)
cases_2016_Italy <- sapply(select(s30,total.cases),na.rm=T,max)-sapply(select(s29,total.cases),na.rm=T,max)

Creating a seperate dataframe for deaths in each year

deaths_2014 <- c(deaths_2014_Guinea,deaths_2014_Nigeria,deaths_2014_Sierra,deaths_2014_Liberia,deaths_2014_Senegal,deaths_2014_USA,
                 deaths_2014_Spain,deaths_2014_Mali,deaths_2014_UnitedKingdom ,deaths_2014_Italy    )


deaths_2015 <- c(deaths_2015_Guinea,deaths_2015_Nigeria,deaths_2015_Sierra,deaths_2015_Liberia,deaths_2015_Senegal,deaths_2015_USA,
                 deaths_2015_Spain,deaths_2015_Mali,deaths_2015_UnitedKingdom ,deaths_2015_Italy    )


deaths_2016 <- c(deaths_2016_Guinea,deaths_2016_Nigeria,deaths_2016_Sierra,deaths_2016_Liberia,deaths_2016_Senegal,deaths_2016_USA,
                 deaths_2016_Spain,deaths_2016_Mali,deaths_2016_UnitedKingdom,deaths_2016_Italy    )

death.Ebola <- data.frame(country_list,deaths_2014,deaths_2015,deaths_2016)
death.Ebola <- do.call(data.frame, lapply(death.Ebola, function(x) {replace(x, is.infinite(x) | is.na(x), 0)}))

invalid factor level, NA generated

death.Ebola

Creating a dataframe for cases in each year

cases_2014 <- c(cases_2014_Guinea,cases_2014_Nigeria,cases_2014_Sierra,cases_2014_Liberia,cases_2014_Senegal,cases_2014_USA,cases_2014_Spain,
                 cases_2014_Mali,cases_2014_UnitedKingdom ,cases_2014_Italy    )


cases_2015 <- c(cases_2015_Guinea,cases_2015_Nigeria,cases_2015_Sierra,cases_2015_Liberia,cases_2015_Senegal,cases_2015_USA,cases_2015_Spain,
                 cases_2015_Mali,cases_2015_UnitedKingdom ,cases_2015_Italy    )


cases_2016 <- c(cases_2016_Guinea,cases_2016_Nigeria,cases_2016_Sierra,cases_2016_Liberia,cases_2016_Senegal,cases_2016_USA,cases_2016_Spain,
                 cases_2016_Mali,cases_2016_UnitedKingdom,cases_2016_Italy    )



case.Ebola <- data.frame(country_list,cases_2014,cases_2015,cases_2016)
case.Ebola <- do.call(data.frame, lapply(case.Ebola, function(x) {replace(x, is.infinite(x) | is.na(x), 0)}))

invalid factor level, NA generated

case.Ebola

case.Ebola$cases_2015[case.Ebola$cases_2015 ==-2] <- 0
case.Ebola$cases_2016[case.Ebola$cases_2016 ==-6] <- 0
case.Ebola

NA
NA

Plot for total deaths in year 2014

library(ggplot2)
library(plotly)
pl <- ggplot(death.Ebola, aes(x=Country, y=deaths_2014))

pl2 <- pl+geom_bar(stat = 'identity',fill = '#fc0373', alpha= 0.95)

pl3 <- pl2 + xlab('Country')+ylab('No of deaths in 2014 ')

pl4<- pl3+ggtitle(" Number of deaths in year 2014 due to outbreak of Ebola virus")

print( pl4 )

Plot for total deaths in year 2015

pl <- ggplot(death.Ebola, aes(x=Country, y=deaths_2015))

pl2 <- pl+geom_bar(stat = 'identity',fill = '#a3cc31', alpha= 0.95)

pl3 <- pl2 + xlab('Country')+ylab('No of deaths ')

pl4<- pl3+ggtitle(" Number of deaths in year 2015 due to outbreak of Ebola virus")

print( pl4 )

Plot for total deaths in year 2015

Since, there were no deaths recoreded in 2016, our plot shows nothing.

pl <- ggplot(death.Ebola, aes(x=Country, y=deaths_2016))

pl2 <- pl+geom_bar(stat = 'identity',fill = '#cc314d', alpha= 0.95)

pl3 <- pl2 + xlab('Country')+ylab('No of deaths ')

pl4<- pl3+ggtitle(" Number of deaths in year 2016 due to outbreak of Ebola virus")

gpl<- ggplotly(pl4)

print( gpl )

NULL

Plot for cases in 2014

pl <- ggplot(case.Ebola, aes(x=Country, y=cases_2014))

pl2 <- pl+geom_bar(stat = 'identity',fill='#cc314d',alpha= 0.95)

pl3 <- pl2 + xlab('Country')+ylab('No of Cases ')

pl4<- pl3+ggtitle(" Number of Cases in year 2014 due to outbreak of Ebola virus ")

gpl<- ggplotly(pl4)

print( pl4 )

Plot for cases in 2015

pl <- ggplot(case.Ebola, aes(x=Country, y=cases_2015))

pl2 <- pl+geom_bar(stat = 'identity',fill='#fa02cd',alpha= 0.95)

pl3 <- pl2 + xlab('Country')+ylab('No of Cases ')

pl4<- pl3+ggtitle(" Number of Cases in year 2015 due to outbreak of Ebola virus ")

gpl<- ggplotly(pl4)

print( pl4 )

Plot for cases in 2016

Since, no cases were recorded in 2016, our plot shows nothing

pl <- ggplot(case.Ebola, aes(x=Country, y=cases_2016))

pl2 <- pl+geom_bar(stat = 'identity',fill='#fa02cd',alpha= 0.95)

pl3 <- pl2 + xlab('Country')+ylab('No of Cases ')

pl4<- pl3+ggtitle(" Number of Cases in year 2016 due to outbreak of Ebola virus(2014-2016) ")

gpl<- ggplotly(pl4)

print( pl4 )

Plot for suspected Vs confirmed deaths during the outbreak

library(ggplot2)
library(plotly)

pl <- ggplot(Ebola, aes(x=total.deaths, y=total.cases ))

pl2 <- pl+geom_smooth(model=lm,color="Blue", fill="Red")

Ignoring unknown parameters: model

pl3 <- pl2 + xlab('Total deaths')+ylab("Total cases")+ggtitle("Plot for total cases vs total deaths")

gpl<- ggplotly(pl3)

`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Removed 8 rows containing non-finite values (stat_smooth).

print(pl3)

Plot for total deaths due Ebola virus

library(ggplot2)
library(plotly)

pl <- ggplot(Ebola, aes(y=total.deaths,x= Year))

pl2 <- pl+geom_bar(stat = "identity",color="red") 
pl3 <- pl2 + xlab('Year')+ylab("Total deaths")+ggtitle("Plot for  total deaths")

gpl<- ggplotly(pl3)
print(gpl)

NULL

Plot for total Cases

library(ggplot2)
library(plotly)

pl <- ggplot(Ebola, aes(y=total.cases,x= Year))

pl2 <- pl+geom_bar(stat = "identity",color="red") 
pl3 <- pl2 + xlab('Year')+ylab("Total cases")+ggtitle("Plot for  total cases")

gpl<- ggplotly(pl3)

Removed 8 rows containing missing values (position_stack).

print(pl3)

df.2014 <-  data.frame(country_list,case.Ebola$cases_2014,death.Ebola$deaths_2014)
df.2014 <- do.call(data.frame, lapply(df.2014, function(x) {replace(x, is.infinite(x) | is.na(x), 0)}))

invalid factor level, NA generated

 df.2014

NA

Pie chart for mortality rate in year 2014

death.percentage.2014 = c(sum(df.2014$deaths_2014)/sum(df.2014$cases_2014), (sum(df.2014$cases_2014) - sum(df.2014$deaths_2014))/sum(df.2014$cases_2014))
death.percentage.2014

[1] 0.3911624 0.6088376

library(plotrix)
Group <- c("Death%","Recorved%")

lbls <- paste0(Group, " ", round(death.percentage.2014 / sum(death.percentage.2014) * 100, 1), "%")
pie3D(death.percentage.2014,labels=lbls,explode=0.1,
   main="Death Vs Recorverd in 2014 ")

df.2015 <-  data.frame(country_list,case.Ebola$cases_2015,death.Ebola$deaths_2015)
df.2015 <- do.call(data.frame, lapply(df.2015, function(x) {replace(x, is.infinite(x) | is.na(x), 0)}))

invalid factor level, NA generated

 print(df.2015)

NA

death.percentage.2015 = c(sum(df.2015$deaths_2015)/sum(df.2015$cases_2015), (sum(df.2015$cases_2015) - sum(df.2015$deaths_2015))/sum(df.2015$cases_2015))
print(death.percentage.2015)

[1] 0.4044624 0.5955376

Pie chart for mortality rate in year 2015

library(plotrix)
Group <- c("Death%","Recorved%")

lbls <- paste0(Group, " ", round(death.percentage.2015 / sum(death.percentage.2015) * 100, 1), "%")
pie3D(death.percentage.2015,labels=lbls,explode=0.1,main="Death Vs Recorverd in 2015 ")

df.2016 <-  data.frame(country_list,case.Ebola$cases_2016,death.Ebola$deaths_2016)
df.2016 <- do.call(data.frame, lapply(df.2016, function(x) {replace(x, is.infinite(x) | is.na(x), 0)}))

invalid factor level, NA generated

 print(df.2016)

death.percentage.2016 = c(sum(df.2016$deaths_2016)/sum(df.2016$cases_2016), (sum(df.2016$cases_2016) - sum(df.2016$deaths_2016))/sum(df.2016$cases_2016))
print(death.percentage.2016)

[1] 0 1

Pie chart for mortality rate in year 2016

library(plotrix)
lbls <- c("Recorved 100%")
pie3D(death.percentage.2016,labels=lbls,main="Death Vs Recorverd in 2016 ")

death.percentage = c(sum(Ebola$total.deaths)/sum(Ebola$total.cases), (sum(Ebola$total.cases) - sum(Ebola$total.deaths))/sum(Ebola$total.cases))
death.percentage

[1] 0.4039933 0.5960067

Pie chart for total mortality rate during the Ebola Outbreak(2014- 2016)

library(plotrix)
Group <- c("Death%","Recorved%")

lbls <- paste0(Group, " ", round(death.percentage / sum(death.percentage) * 100, 1), "%")
pie3D(death.percentage,labels=lbls,explode=0.1,main="Death Vs Recorverd (2014,2015 and 2016) ")